A fuzzy logic controller with interval-valued inference mechanism is presented for the control of distributed parameter system (DPS). Interval-valued fuzzy set, a special case of type-2 fuzzy set, is innovatively designed to cope with the spatial information of distributed parameter systems. The proposed fuzzy logic controller is composed of six modules: fuzzifier, fuzzy composition, rules, inf...

Based on parameter self-tuning fuzzy PID controller, a fuzzy inference method is utilized to realize automatic regulating PID parameter, and the application of the controller in a system is studied with MATLAB in this paper. The combination of PID controller system and fuzzy controller system combines the convenience of PID control together with the flexibility of fuzzy control, and takes advan...

Abstract: A fuzzy test for testing statistical hypotheses about an imprecise parameter is proposed for the case when the available data are also imprecise. The proposed method is based on the relationship between the acceptance region of statistical tests at level β and confidence intervals for the parameter of interest at confidence level 1 − β. First, a fuzzy confidence interval is constructe...

Fuzzy logic has been a useful tool for modeling complex problems, with the use of fuzzy variables and fuzzy rules, and in this paper we use a fuzzy system for parameter adaptation in the Ant Colony Optimization (ACO metaheuristic). In this case we perform the dynamic adaptation of Alpha and Rho parameters; this is to control the abilities of ACO to perform a global and local search. Simulation ...

The goal of this study was to design a practical fuzzy controller of the cycle-to-cycle control for multi-joint movements of swing phase of functional electrical stimulation (FES) induced gait. First, we designed three fuzzy controllers (a fixed fuzzy controller, a fuzzy controller with parameter adjustment based on the gradient descent method, and a fuzzy controller with parameter adjustment b...

An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented. The fuzzy GAP coding algorithm is compared to the dynamic parameter encoding scheme proposed by Schraudolph and Belew. The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated. Fuzzy GAP coding control is shown to dramatically in...

Fuzzy modeling generally comprises structure identification and parameter identification. The former determines the structure of a rule-base, whereas the latter determines the contents of each rule. Applying neural networks or genetic algorithms to identify the parameter sets and structures of a fuzzy system is increasingly popular owing to their ability to learn and adapt. However, most conven...

In this paper, we propose a new method to specify the sequence of parameter values for a fuzzy clustering algorithm by using Q-learning. In the clustering algorithm, we employ similarities between two data points and distances from data to cluster centers as the fuzzy clustering criteria. The fuzzy clustering is achieved by optimizing an objective function which is solved by the Picard iteratio...